Business Intelligence (BI) systems are designed to help organizations run better by connecting people to the information they need to make better decisions. As the amount of data rapidly develops, and enterprises place ever-increasing demands on their BI systems, current data scales and complexity are increasing dramatically. In some instances prior to formal BI systems, only a small set of highly skilled people could access enterprise data, and the expertise (writing native code and SQL) they may have encoded did not scale across the organization. This is an example of how, traditional management approaches crossing core data assets involve inflexible and rigid hierarchical manager/agent formations, relying on significant human intervention and analysis, which become increasingly difficult as scale and complexity grows.
In some aspects, an increased reliance on asynchronous event-based analysis in BI systems requires more real-time, complex event processing to filter and analyze event streams to provide relevant information to users. If performed at the application level, this event processing is expensive, both in terms of the computation required and the cost of existing commercial solutions. Furthermore, heterogeneous format of event and domain expertise encoding (i.e. rules or policies) may exist in the same organization and lead to an interpretation issue across systems/data warehouses. Thus new solutions are desired to make complex event processing affordable and easy, while maintaining the expressiveness and scalability of other distributed, event-based systems. This is especially the case for semantic event processing, where the performance is a still a challenge for the real-time processing with large amount of events (i.e., big data).